Note
Go to the end to download the full example code.
How to use NIDL with Hydra¶
This tutorial shows how to build a full NIDL experiment using Hydra configurations. Hydra allows you to describe datasets, transforms, dataloaders, and models directly in YAML and instantiate them at runtime.
The goal is to understand:
how Hydra instantiates Python objects using
_target_.how
${...}references work inside configs.how YAML anchors (
&name), aliases (*name) and merges (<<: *name) help avoid duplication.how NIDL datasets, transforms, and models can be composed declaratively.
Hydra Concepts Explained¶
1. ``_target_`` - instantiate Python objects from YAML
Hydra can instantiate Python objects directly from configuration files.
Any dictionary containing a _target_ key is interpreted as a
description of a Python class or function to be constructed at runtime.
For example:
encoder:
_target_: torchvision.ops.MLP
in_channels: 100
hidden_channels: [64, 32]
This means: “create an instance of torchvision.ops.MLP and pass it the
arguments in_channels=100 and hidden_channels=[64, 32].”
During execution, Hydra resolves this block using
hydra.utils.instantiate:
from hydra.utils import instantiate
encoder = instantiate(cfg.encoder)
This mechanism allows entire components—datasets, transforms, dataloaders, models—to be declared declaratively in YAML and built automatically when the experiment runs.
2. ``${…}`` - reference previously defined config values
Hydra allows you to reuse values defined elsewhere in the configuration
using the ${...} syntax.
For example:
argument:
noise_std: 0.5
transform:
_target_: RandomGaussianNoise
noise_std: ${augment.noise_std}
Here, ${augment.noise_std} is replaced with 0.5 during
configuration resolution. The instantiated RandomGaussianNoise object
therefore receives noise_std=0.5 automatically.
This mechanism ensures that shared parameters (such as augmentation strengths, dataset paths, or model dimensions) remain synchronized across the entire Hydra configuration.
3. YAML anchors ``&name`` and aliases ``*name`` or merges ``<<: *name``
YAML anchors let you define reusable configuration blocks that can be referenced later. This keeps Hydra configs concise.
You create an anchor using &name:
_base_dataset: &base_ds
_target_: nidl.datasets.TabularDataset
root: ${data.root}
You can then reuse this block in two different ways.
Using *name simply inserts the anchored block as a value:
dataset:
train: *_base_dataset
Using <<: *name merges the anchored dictionary into the current one
and allows overriding or adding fields:
dataset:
<<: *base_ds
split: "train"
This means: “start from the contents of base_ds and then override
the split field.”
Imports¶
from pathlib import Path
import hydra
from omegaconf import DictConfig
from hydra.utils import instantiate
import numpy as np
Transforms¶
These are simple NumPy‑based transforms used in the Hydra config.
class Flatten:
def __call__(self, x):
return x.flatten()
class RandomMask:
def __init__(self, mask_prob):
self.mask_prob = mask_prob
def __call__(self, x):
mask = (np.random.rand(*x.shape) > self.mask_prob).astype(np.float32)
return x * mask
class RandomGaussianNoise:
def __init__(self, noise_std):
self.noise_std = noise_std
def __call__(self, x):
if np.random.rand() > 0.5:
noise = np.random.randn(*x.shape) * self.noise_std
return x + noise.astype(np.float32)
return x
class SBMTransform:
def __init__(self, channels):
self.channels = channels
def __call__(self, x):
return x[self.channels].flatten()
Generate Hydra Config¶
We generate a YAML config so that Sphinx-Gallery can display it.
config_text = r"""
data:
data_dir: "/tmp/openBHB"
batch_size: 32
num_workers: 4
augment:
mask_prob: 0.8
noise_std: 0.5
n_views: 2
channels:
- 0
- 1
- 2
- 5
_base_vbm_transform: &vbm_transform
_target_: torchvision.transforms.Compose
transforms:
- _target_: __main__.Flatten
_base_sbm_transform: &sbm_transform
_target_: torchvision.transforms.Compose
transforms:
- _target_: __main__.SBMTransform
channels: ${augment.channels}
- _target_: __main__.Flatten
contrast:
_target_: torchvision.transforms.Compose
transforms:
- _target_: __main__.Flatten
- _target_: __main__.RandomMask
mask_prob: ${augment.mask_prob}
- _target_: __main__.RandomGaussianNoise
noise_std: ${augment.noise_std}
_base_vbm_ssl_transform: &vbm_ssl_transform
_target_: nidl.transforms.MultiViewsTransform
transforms:
_target_: torchvision.transforms.Compose
transforms:
- ${augment._base_vbm_transform}
- ${augment.contrast}
n_views: ${augment.n_views}
_base_sbm_ssl_transform: &sbm_ssl_transform
_target_: nidl.transforms.MultiViewsTransform
transforms:
_target_: torchvision.transforms.Compose
transforms:
- ${augment._base_sbm_transform}
- ${augment.contrast}
n_views: ${augment.n_views}
dataset:
_base_openbhb: &openbhb_base
_target_: nidl.datasets.OpenBHB
root: ${data.data_dir}
streaming: false
ssl_vbm_train:
<<: *openbhb_base
target: "age"
modality: "vbm_roi"
transforms: *vbm_ssl_transform
ssl_vbm_val:
<<: *openbhb_base
target: "age"
modality: "vbm_roi"
split: "val"
transforms: *vbm_ssl_transform
ssl_sbm_train:
<<: *openbhb_base
target: "age"
modality: "fs_desikan_roi"
transforms: *sbm_ssl_transform
ssl_sbm_val:
<<: *openbhb_base
target: "age"
modality: "fs_desikan_roi"
split: "val"
transforms: *sbm_ssl_transform
vbm_test:
<<: *openbhb_base
target: null
modality: "vbm_roi"
split: "val"
transforms: *vbm_transform
sbm_test:
<<: *openbhb_base
target: null
modality: "fs_desikan_roi"
split: "val"
transforms: *sbm_transform
dataloader:
ssl_vbm_train:
_target_: torch.utils.data.DataLoader
dataset: ${dataset.ssl_vbm_train}
batch_size: ${data.batch_size}
num_workers: ${data.num_workers}
shuffle: true
ssl_vbm_val:
_target_: torch.utils.data.DataLoader
dataset: ${dataset.ssl_vbm_val}
batch_size: ${data.batch_size}
num_workers: ${data.num_workers}
shuffle: false
ssl_sbm_train:
_target_: torch.utils.data.DataLoader
dataset: ${dataset.ssl_sbm_train}
batch_size: ${data.batch_size}
num_workers: ${data.num_workers}
shuffle: true
ssl_sbm_val:
_target_: torch.utils.data.DataLoader
dataset: ${dataset.ssl_sbm_val}
batch_size: ${data.batch_size}
num_workers: ${data.num_workers}
shuffle: false
vbm_test:
_target_: torch.utils.data.DataLoader
dataset: ${dataset.vbm_test}
batch_size: ${data.batch_size}
num_workers: ${data.num_workers}
shuffle: false
sbm_test:
_target_: torch.utils.data.DataLoader
dataset: ${dataset.sbm_test}
batch_size: ${data.batch_size}
num_workers: ${data.num_workers}
shuffle: false
model:
latent_size: 32
sigma: 4
max_epochs: 2
vbm_encoder:
_target_: torchvision.ops.MLP
in_channels: 284
hidden_channels:
- 64
- ${model.latent_size}
sbm_encoder:
_target_: torchvision.ops.MLP
in_channels: 272
hidden_channels:
- 64
- ${model.latent_size}
vbm:
_target_: nidl.estimators.ssl.YAwareContrastiveLearning
encoder: ${model.vbm_encoder}
proj_input_dim: ${model.latent_size}
proj_hidden_dim: ${model.latent_size}
proj_output_dim: ${model.latent_size}
bandwidth: ${model.sigma}
random_state: 42
max_epochs: ${model.max_epochs}
temperature: 0.1
learning_rate: 1e-5
enable_checkpointing: false
sbm:
_target_: nidl.estimators.ssl.YAwareContrastiveLearning
encoder: ${model.sbm_encoder}
proj_input_dim: ${model.latent_size}
proj_hidden_dim: ${model.latent_size}
proj_output_dim: ${model.latent_size}
bandwidth: ${model.sigma}
random_state: 42
max_epochs: ${model.max_epochs}
temperature: 0.1
learning_rate: 1e-5
enable_checkpointing: false
"""
tmpdir = Path("/tmp")
config_path = tmpdir / "openbhb_config.yaml"
config_path.write_text(config_text)
print(config_text)
data:
data_dir: "/tmp/openBHB"
batch_size: 32
num_workers: 4
augment:
mask_prob: 0.8
noise_std: 0.5
n_views: 2
channels:
- 0
- 1
- 2
- 5
_base_vbm_transform: &vbm_transform
_target_: torchvision.transforms.Compose
transforms:
- _target_: __main__.Flatten
_base_sbm_transform: &sbm_transform
_target_: torchvision.transforms.Compose
transforms:
- _target_: __main__.SBMTransform
channels: ${augment.channels}
- _target_: __main__.Flatten
contrast:
_target_: torchvision.transforms.Compose
transforms:
- _target_: __main__.Flatten
- _target_: __main__.RandomMask
mask_prob: ${augment.mask_prob}
- _target_: __main__.RandomGaussianNoise
noise_std: ${augment.noise_std}
_base_vbm_ssl_transform: &vbm_ssl_transform
_target_: nidl.transforms.MultiViewsTransform
transforms:
_target_: torchvision.transforms.Compose
transforms:
- ${augment._base_vbm_transform}
- ${augment.contrast}
n_views: ${augment.n_views}
_base_sbm_ssl_transform: &sbm_ssl_transform
_target_: nidl.transforms.MultiViewsTransform
transforms:
_target_: torchvision.transforms.Compose
transforms:
- ${augment._base_sbm_transform}
- ${augment.contrast}
n_views: ${augment.n_views}
dataset:
_base_openbhb: &openbhb_base
_target_: nidl.datasets.OpenBHB
root: ${data.data_dir}
streaming: false
ssl_vbm_train:
<<: *openbhb_base
target: "age"
modality: "vbm_roi"
transforms: *vbm_ssl_transform
ssl_vbm_val:
<<: *openbhb_base
target: "age"
modality: "vbm_roi"
split: "val"
transforms: *vbm_ssl_transform
ssl_sbm_train:
<<: *openbhb_base
target: "age"
modality: "fs_desikan_roi"
transforms: *sbm_ssl_transform
ssl_sbm_val:
<<: *openbhb_base
target: "age"
modality: "fs_desikan_roi"
split: "val"
transforms: *sbm_ssl_transform
vbm_test:
<<: *openbhb_base
target: null
modality: "vbm_roi"
split: "val"
transforms: *vbm_transform
sbm_test:
<<: *openbhb_base
target: null
modality: "fs_desikan_roi"
split: "val"
transforms: *sbm_transform
dataloader:
ssl_vbm_train:
_target_: torch.utils.data.DataLoader
dataset: ${dataset.ssl_vbm_train}
batch_size: ${data.batch_size}
num_workers: ${data.num_workers}
shuffle: true
ssl_vbm_val:
_target_: torch.utils.data.DataLoader
dataset: ${dataset.ssl_vbm_val}
batch_size: ${data.batch_size}
num_workers: ${data.num_workers}
shuffle: false
ssl_sbm_train:
_target_: torch.utils.data.DataLoader
dataset: ${dataset.ssl_sbm_train}
batch_size: ${data.batch_size}
num_workers: ${data.num_workers}
shuffle: true
ssl_sbm_val:
_target_: torch.utils.data.DataLoader
dataset: ${dataset.ssl_sbm_val}
batch_size: ${data.batch_size}
num_workers: ${data.num_workers}
shuffle: false
vbm_test:
_target_: torch.utils.data.DataLoader
dataset: ${dataset.vbm_test}
batch_size: ${data.batch_size}
num_workers: ${data.num_workers}
shuffle: false
sbm_test:
_target_: torch.utils.data.DataLoader
dataset: ${dataset.sbm_test}
batch_size: ${data.batch_size}
num_workers: ${data.num_workers}
shuffle: false
model:
latent_size: 32
sigma: 4
max_epochs: 2
vbm_encoder:
_target_: torchvision.ops.MLP
in_channels: 284
hidden_channels:
- 64
- ${model.latent_size}
sbm_encoder:
_target_: torchvision.ops.MLP
in_channels: 272
hidden_channels:
- 64
- ${model.latent_size}
vbm:
_target_: nidl.estimators.ssl.YAwareContrastiveLearning
encoder: ${model.vbm_encoder}
proj_input_dim: ${model.latent_size}
proj_hidden_dim: ${model.latent_size}
proj_output_dim: ${model.latent_size}
bandwidth: ${model.sigma}
random_state: 42
max_epochs: ${model.max_epochs}
temperature: 0.1
learning_rate: 1e-5
enable_checkpointing: false
sbm:
_target_: nidl.estimators.ssl.YAwareContrastiveLearning
encoder: ${model.sbm_encoder}
proj_input_dim: ${model.latent_size}
proj_hidden_dim: ${model.latent_size}
proj_output_dim: ${model.latent_size}
bandwidth: ${model.sigma}
random_state: 42
max_epochs: ${model.max_epochs}
temperature: 0.1
learning_rate: 1e-5
enable_checkpointing: false
Main Experiment¶
Hydra loads the configuration file and instantiates all objects.
@hydra.main(
config_path="/tmp",
config_name="openbhb_config",
version_base="1.3",
)
def main(cfg: DictConfig):
# Instantiate dataloaders
ssl_vbm_train = instantiate(cfg.dataloader.ssl_vbm_train)
ssl_vbm_val = instantiate(cfg.dataloader.ssl_vbm_val)
ssl_sbm_train = instantiate(cfg.dataloader.ssl_sbm_train)
ssl_sbm_val = instantiate(cfg.dataloader.ssl_sbm_val)
vbm_test = instantiate(cfg.dataloader.vbm_test)
sbm_test = instantiate(cfg.dataloader.sbm_test)
# Instantiate models
vbm_model = instantiate(cfg.model.vbm)
sbm_model = instantiate(cfg.model.sbm)
# Fit models
vbm_model.fit(ssl_vbm_train, ssl_vbm_val)
sbm_model.fit(ssl_sbm_train, ssl_sbm_val)
# Compute embeddings
z_vbm_test = vbm_model.transform(vbm_test)
z_sbm_test = sbm_model.transform(sbm_test)
print(f"Z shapes - VBM: {z_vbm_test.shape}, SBM: {z_sbm_test.shape}")
Run Example¶
main()
[2026-09-18 19:13:22,821][httpx][INFO] - HTTP Request: HEAD https://huggingface.co/datasets/benoit-dufumier/openBHB/resolve/8508cda68fea74f217926acbf46ee5863f8879d1/participants.tsv "HTTP/1.1 307 Temporary Redirect"
[2026-09-18 19:13:22,830][httpx][INFO] - HTTP Request: HEAD https://huggingface.co/api/resolve-cache/datasets/benoit-dufumier/openBHB/8508cda68fea74f217926acbf46ee5863f8879d1/participants.tsv?%2Fdatasets%2Fbenoit-dufumier%2FopenBHB%2Fresolve%2F8508cda68fea74f217926acbf46ee5863f8879d1%2Fparticipants.tsv=&etag=%221184250276479fc84b74d316899c4b8ab2974f5e%22 "HTTP/1.1 200 OK"
[2026-09-18 19:13:22,840][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/resolve-cache/datasets/benoit-dufumier/openBHB/8508cda68fea74f217926acbf46ee5863f8879d1/participants.tsv?%2Fdatasets%2Fbenoit-dufumier%2FopenBHB%2Fresolve%2F8508cda68fea74f217926acbf46ee5863f8879d1%2Fparticipants.tsv=&etag=%221184250276479fc84b74d316899c4b8ab2974f5e%22 "HTTP/1.1 200 OK"
[2026-09-18 19:13:22,948][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?recursive=true&expand=false "HTTP/1.1 200 OK"
[2026-09-18 19:13:23,168][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0TWpNNU5EZzVOVEV4TnprMUlpd2lkSEpsWlY5dmFXUWlPaUprT0RWaFpqVTRaRGhpTWpnMk5qY3daamRrTVRGak9EbGpaR0V5TVRVeFlXRXlPVGsyTldObUluMD06MTAwMA%3D%3D "HTTP/1.1 200 OK"
[2026-09-18 19:13:23,323][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0TXpjNE5qQXpPRFF3TlRVeUlpd2lkSEpsWlY5dmFXUWlPaUprT0RWaFpqVTRaRGhpTWpnMk5qY3daamRrTVRGak9EbGpaR0V5TVRVeFlXRXlPVGsyTldObUluMD06MjAwMA%3D%3D "HTTP/1.1 200 OK"
[2026-09-18 19:13:23,618][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0TlRFM05qSTNNamM1T0RReUlpd2lkSEpsWlY5dmFXUWlPaUprT0RWaFpqVTRaRGhpTWpnMk5qY3daamRrTVRGak9EbGpaR0V5TVRVeFlXRXlPVGsyTldObUluMD06MzAwMA%3D%3D "HTTP/1.1 200 OK"
[2026-09-18 19:13:23,860][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0TmpZMU1qTXdOamd3TWpJMElpd2lkSEpsWlY5dmFXUWlPaUprT0RWaFpqVTRaRGhpTWpnMk5qY3daamRrTVRGak9EbGpaR0V5TVRVeFlXRXlPVGsyTldObUluMD06NDAwMA%3D%3D "HTTP/1.1 200 OK"
[2026-09-18 19:13:24,003][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0T0RBd05USXdOREV4T0RJeklpd2lkSEpsWlY5dmFXUWlPaUprT0RWaFpqVTRaRGhpTWpnMk5qY3daamRrTVRGak9EbGpaR0V5TVRVeFlXRXlPVGsyTldObUluMD06NTAwMA%3D%3D "HTTP/1.1 200 OK"
[2026-09-18 19:13:24,251][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0T1RNMk56QXpNalF4TVRRd0lpd2lkSEpsWlY5dmFXUWlPaUprT0RWaFpqVTRaRGhpTWpnMk5qY3daamRrTVRGak9EbGpaR0V5TVRVeFlXRXlPVGsyTldObUluMD06NjAwMA%3D%3D "HTTP/1.1 200 OK"
[2026-09-18 19:13:24,459][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5eVlYZGtZWFJoTDNOMVlpMHhPREExTWpVMk1EZ3lPVFF2YzJWekxURWlMQ0owY21WbFgyOXBaQ0k2SW1RNE5XRm1OVGhrT0dJeU9EWTJOekJtTjJReE1XTTRPV05rWVRJeE5URmhZVEk1T1RZMVkyWWlmUT09OjcwMDA%3D "HTTP/1.1 200 OK"
[2026-09-18 19:13:24,644][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5eVlYZGtZWFJoTDNOMVlpMHpNVGszTVRZMU1qSTRNVFF2YzJWekxURWlMQ0owY21WbFgyOXBaQ0k2SW1RNE5XRm1OVGhrT0dJeU9EWTJOekJtTjJReE1XTTRPV05rWVRJeE5URmhZVEk1T1RZMVkyWWlmUT09OjgwMDA%3D "HTTP/1.1 200 OK"
[2026-09-18 19:13:24,814][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5eVlYZGtZWFJoTDNOMVlpMDBOVGd5TXpBd01qYzJORGt2YzJWekxURWlMQ0owY21WbFgyOXBaQ0k2SW1RNE5XRm1OVGhrT0dJeU9EWTJOekJtTjJReE1XTTRPV05rWVRJeE5URmhZVEk1T1RZMVkyWWlmUT09OjkwMDA%3D "HTTP/1.1 200 OK"
[2026-09-18 19:13:24,985][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5eVlYZGtZWFJoTDNOMVlpMDFPVFkwTXprME1UZ3lNVFF2YzJWekxURWlMQ0owY21WbFgyOXBaQ0k2SW1RNE5XRm1OVGhrT0dJeU9EWTJOekJtTjJReE1XTTRPV05rWVRJeE5URmhZVEk1T1RZMVkyWWlmUT09OjEwMDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:25,127][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5eVlYZGtZWFJoTDNOMVlpMDNOREUxTkRFd01EVTJOREV2YzJWekxURWlMQ0owY21WbFgyOXBaQ0k2SW1RNE5XRm1OVGhrT0dJeU9EWTJOekJtTjJReE1XTTRPV05rWVRJeE5URmhZVEk1T1RZMVkyWWlmUT09OjExMDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:25,266][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5eVlYZGtZWFJoTDNOMVlpMDROekkwT1RreU1qWXlOVEF2YzJWekxURWlMQ0owY21WbFgyOXBaQ0k2SW1RNE5XRm1OVGhrT0dJeU9EWTJOekJtTjJReE1XTTRPV05rWVRJeE5URmhZVEk1T1RZMVkyWWlmUT09OjEyMDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:25,434][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjJZV3d2WkdWeWFYWmhkR2wyWlhNdmMzVmlMVEUwT1RnMU9UUTJOVEkyTUM5elpYTXRNU0lzSW5SeVpXVmZiMmxrSWpvaVpEZzFZV1kxT0dRNFlqSTROalkzTUdZM1pERXhZemc1WTJSaE1qRTFNV0ZoTWprNU5qVmpaaUo5OjEzMDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:25,578][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjJZV3d2WkdWeWFYWmhkR2wyWlhNdmMzVmlMVGMyTVRNek9ESXdNVGczTWk5elpYTXRNU0lzSW5SeVpXVmZiMmxrSWpvaVpEZzFZV1kxT0dRNFlqSTROalkzTUdZM1pERXhZemc1WTJSaE1qRTFNV0ZoTWprNU5qVmpaaUo5OjE0MDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:25,810][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjJZV3d2Y21GM1pHRjBZUzl6ZFdJdE5EVTBOREU1TURrek1EazRJaXdpZEhKbFpWOXZhV1FpT2lKa09EVmhaalU0WkRoaU1qZzJOamN3Wmpka01URmpPRGxqWkdFeU1UVXhZV0V5T1RrMk5XTm1JbjA9OjE1MDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:26,076][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0TVRBeE9UUXlNRE13T0RjeEwzTmxjeTB4TDNOMVlpMHhNREU1TkRJd016QTROekZmY0hKbGNISnZZeTF4ZFdGemFYSmhkMTlVTVhjdWJuQjVJaXdpZEhKbFpWOXZhV1FpT2lKa09EVmhaalU0WkRoaU1qZzJOamN3Wmpka01URmpPRGxqWkdFeU1UVXhZV0V5T1RrMk5XTm1JbjA9OjE2MDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:26,394][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0TVRRNU5USXlNREEzTXpJM0wzTmxjeTB4TDNOMVlpMHhORGsxTWpJd01EY3pNamRmY0hKbGNISnZZeTFtY21WbGMzVnlabVZ5WDJSbGMyTXRaR1Z6ZEhKcFpYVjRYMUpQU1M1dWNIa2lMQ0owY21WbFgyOXBaQ0k2SW1RNE5XRm1OVGhrT0dJeU9EWTJOekJtTjJReE1XTTRPV05rWVRJeE5URmhZVEk1T1RZMVkyWWlmUT09OjE3MDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:26,666][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0TVRrNE9EZ3lOVFExTURJd0wzTmxjeTB4TDNOMVlpMHhPVGc0T0RJMU5EVXdNakJmY0hKbGNISnZZeTFqWVhReE1uWmliVjlrWlhOakxXZHRYMVF4ZHk1dWNIa2lMQ0owY21WbFgyOXBaQ0k2SW1RNE5XRm1OVGhrT0dJeU9EWTJOekJtTjJReE1XTTRPV05rWVRJeE5URmhZVEk1T1RZMVkyWWlmUT09OjE4MDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:26,916][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0TWpReE5EVTJOVEEyTURFeEwzTmxjeTB4TDNOMVlpMHlOREUwTlRZMU1EWXdNVEZmY0hKbGNISnZZeTF4ZFdGemFYSmhkMTlVTVhjdWJuQjVJaXdpZEhKbFpWOXZhV1FpT2lKa09EVmhaalU0WkRoaU1qZzJOamN3Wmpka01URmpPRGxqWkdFeU1UVXhZV0V5T1RrMk5XTm1JbjA9OjE5MDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:27,116][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0TWpreU9UTXhPVGszTVRnNUwzTmxjeTB4TDNOMVlpMHlPVEk1TXpFNU9UY3hPRGxmY0hKbGNISnZZeTFtY21WbGMzVnlabVZ5WDJSbGMyTXRaR1Z6ZEhKcFpYVjRYMUpQU1M1dWNIa2lMQ0owY21WbFgyOXBaQ0k2SW1RNE5XRm1OVGhrT0dJeU9EWTJOekJtTjJReE1XTTRPV05rWVRJeE5URmhZVEk1T1RZMVkyWWlmUT09OjIwMDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:27,309][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0TXpNNE1EYzRPREl4TmpVNUwzTmxjeTB4TDNOMVlpMHpNemd3TnpnNE1qRTJOVGxmY0hKbGNISnZZeTFqWVhReE1uWmliVjlrWlhOakxXZHRYMVF4ZHk1dWNIa2lMQ0owY21WbFgyOXBaQ0k2SW1RNE5XRm1OVGhrT0dJeU9EWTJOekJtTjJReE1XTTRPV05rWVRJeE5URmhZVEk1T1RZMVkyWWlmUT09OjIxMDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:27,641][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0TXpnd05EZzBNakl5TWpNM0wzTmxjeTB4TDNOMVlpMHpPREEwT0RReU1qSXlNemRmY0hKbGNISnZZeTF4ZFdGemFYSmhkMTlVTVhjdWJuQjVJaXdpZEhKbFpWOXZhV1FpT2lKa09EVmhaalU0WkRoaU1qZzJOamN3Wmpka01URmpPRGxqWkdFeU1UVXhZV0V5T1RrMk5XTm1JbjA9OjIyMDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:27,862][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0TkRNd01qRXlPVGcyT1RFNUwzTmxjeTB4TDNOMVlpMDBNekF5TVRJNU9EWTVNVGxmY0hKbGNISnZZeTFtY21WbGMzVnlabVZ5WDJSbGMyTXRaR1Z6ZEhKcFpYVjRYMUpQU1M1dWNIa2lMQ0owY21WbFgyOXBaQ0k2SW1RNE5XRm1OVGhrT0dJeU9EWTJOekJtTjJReE1XTTRPV05rWVRJeE5URmhZVEk1T1RZMVkyWWlmUT09OjIzMDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:28,163][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0TkRjM05UUTBORFUzTkRJMkwzTmxjeTB4TDNOMVlpMDBOemMxTkRRME5UYzBNalpmY0hKbGNISnZZeTFqWVhReE1uWmliVjlrWlhOakxXZHRYMVF4ZHk1dWNIa2lMQ0owY21WbFgyOXBaQ0k2SW1RNE5XRm1OVGhrT0dJeU9EWTJOekJtTjJReE1XTTRPV05rWVRJeE5URmhZVEk1T1RZMVkyWWlmUT09OjI0MDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:28,355][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0TlRFNU1EZzFOekl6TmprMEwzTmxjeTB4TDNOMVlpMDFNVGt3T0RVM01qTTJPVFJmY0hKbGNISnZZeTF4ZFdGemFYSmhkMTlVTVhjdWJuQjVJaXdpZEhKbFpWOXZhV1FpT2lKa09EVmhaalU0WkRoaU1qZzJOamN3Wmpka01URmpPRGxqWkdFeU1UVXhZV0V5T1RrMk5XTm1JbjA9OjI1MDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:28,667][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0TlRZeU9ETTVNRGc1TkRVMEwzTmxjeTB4TDNOMVlpMDFOakk0TXprd09EazBOVFJmY0hKbGNISnZZeTFtY21WbGMzVnlabVZ5WDJSbGMyTXRaR1Z6ZEhKcFpYVjRYMUpQU1M1dWNIa2lMQ0owY21WbFgyOXBaQ0k2SW1RNE5XRm1OVGhrT0dJeU9EWTJOekJtTjJReE1XTTRPV05rWVRJeE5URmhZVEk1T1RZMVkyWWlmUT09OjI2MDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:28,874][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0TmpFMk9UZ3hNVGc0TXpJMkwzTmxjeTB4TDNOMVlpMDJNVFk1T0RFeE9EZ3pNalpmY0hKbGNISnZZeTFqWVhReE1uWmliVjlrWlhOakxXZHRYMVF4ZHk1dWNIa2lMQ0owY21WbFgyOXBaQ0k2SW1RNE5XRm1OVGhrT0dJeU9EWTJOekJtTjJReE1XTTRPV05rWVRJeE5URmhZVEk1T1RZMVkyWWlmUT09OjI3MDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:29,101][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0TmpZM01qWTNOVGN5TlRZNUwzTmxjeTB4TDNOMVlpMDJOamN5TmpjMU56STFOamxmY0hKbGNISnZZeTF4ZFdGemFYSmhkMTlVTVhjdWJuQjVJaXdpZEhKbFpWOXZhV1FpT2lKa09EVmhaalU0WkRoaU1qZzJOamN3Wmpka01URmpPRGxqWkdFeU1UVXhZV0V5T1RrMk5XTm1JbjA9OjI4MDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:29,292][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0TnpFeE16RTJNRGc0T0RneEwzTmxjeTB4TDNOMVlpMDNNVEV6TVRZd09EZzRPREZmY0hKbGNISnZZeTFtY21WbGMzVnlabVZ5WDJSbGMyTXRaR1Z6ZEhKcFpYVjRYMUpQU1M1dWNIa2lMQ0owY21WbFgyOXBaQ0k2SW1RNE5XRm1OVGhrT0dJeU9EWTJOekJtTjJReE1XTTRPV05rWVRJeE5URmhZVEk1T1RZMVkyWWlmUT09OjI5MDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:29,487][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0TnpZeE1EQTRPVEkzTmprd0wzTmxjeTB4TDNOMVlpMDNOakV3TURnNU1qYzJPVEJmY0hKbGNISnZZeTFqWVhReE1uWmliVjlrWlhOakxXZHRYMVF4ZHk1dWNIa2lMQ0owY21WbFgyOXBaQ0k2SW1RNE5XRm1OVGhrT0dJeU9EWTJOekJtTjJReE1XTTRPV05rWVRJeE5URmhZVEk1T1RZMVkyWWlmUT09OjMwMDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:29,767][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0T0RBeU1EQTRPRE0zT1RJeEwzTmxjeTB4TDNOMVlpMDRNREl3TURnNE16YzVNakZmY0hKbGNISnZZeTF4ZFdGemFYSmhkMTlVTVhjdWJuQjVJaXdpZEhKbFpWOXZhV1FpT2lKa09EVmhaalU0WkRoaU1qZzJOamN3Wmpka01URmpPRGxqWkdFeU1UVXhZV0V5T1RrMk5XTm1JbjA9OjMxMDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:29,954][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0T0RRNE16QXpOVE14T0Rnd0wzTmxjeTB4TDNOMVlpMDRORGd6TURNMU16RTRPREJmY0hKbGNISnZZeTFtY21WbGMzVnlabVZ5WDJSbGMyTXRaR1Z6ZEhKcFpYVjRYMUpQU1M1dWNIa2lMQ0owY21WbFgyOXBaQ0k2SW1RNE5XRm1OVGhrT0dJeU9EWTJOekJtTjJReE1XTTRPV05rWVRJeE5URmhZVEk1T1RZMVkyWWlmUT09OjMyMDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:30,163][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0T0RrME5qZ3dOamMyTVRZeUwzTmxjeTB4TDNOMVlpMDRPVFEyT0RBMk56WXhOakpmY0hKbGNISnZZeTFqWVhReE1uWmliVjlrWlhOakxXZHRYMVF4ZHk1dWNIa2lMQ0owY21WbFgyOXBaQ0k2SW1RNE5XRm1OVGhrT0dJeU9EWTJOekJtTjJReE1XTTRPV05rWVRJeE5URmhZVEk1T1RZMVkyWWlmUT09OjMzMDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:30,358][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0T1RNNE9EazJPREV4TlRjM0wzTmxjeTB4TDNOMVlpMDVNemc0T1RZNE1URTFOemRmY0hKbGNISnZZeTF4ZFdGemFYSmhkMTlVTVhjdWJuQjVJaXdpZEhKbFpWOXZhV1FpT2lKa09EVmhaalU0WkRoaU1qZzJOamN3Wmpka01URmpPRGxqWkdFeU1UVXhZV0V5T1RrMk5XTm1JbjA9OjM0MDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:30,558][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5a1pYSnBkbUYwYVhabGN5OXpkV0l0T1RnMk5UazRPRE0zTnpBekwzTmxjeTB4TDNOMVlpMDVPRFkxT1RnNE16YzNNRE5mY0hKbGNISnZZeTFtY21WbGMzVnlabVZ5WDJSbGMyTXRaR1Z6ZEhKcFpYVjRYMUpQU1M1dWNIa2lMQ0owY21WbFgyOXBaQ0k2SW1RNE5XRm1OVGhrT0dJeU9EWTJOekJtTjJReE1XTTRPV05rWVRJeE5URmhZVEk1T1RZMVkyWWlmUT09OjM1MDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:30,784][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5eVlYZGtZWFJoTDNOMVlpMHlPVEk1T0RRNU1EZ3pNREF2YzJWekxURXZjM1ZpTFRJNU1qazRORGt3T0RNd01GOVVNWGN1Ym1scExtZDZJaXdpZEhKbFpWOXZhV1FpT2lKa09EVmhaalU0WkRoaU1qZzJOamN3Wmpka01URmpPRGxqWkdFeU1UVXhZV0V5T1RrMk5XTm1JbjA9OjM2MDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:31,062][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5eVlYZGtZWFJoTDNOMVlpMDFOakk1TXpFMU16RTVOVGN2YzJWekxURXZjM1ZpTFRVMk1qa3pNVFV6TVRrMU4xOVVNWGN1Ym1scExtZDZJaXdpZEhKbFpWOXZhV1FpT2lKa09EVmhaalU0WkRoaU1qZzJOamN3Wmpka01URmpPRGxqWkdFeU1UVXhZV0V5T1RrMk5XTm1JbjA9OjM3MDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:31,290][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjBjbUZwYmk5eVlYZGtZWFJoTDNOMVlpMDRORGd6TURRek5qVTNPRFF2YzJWekxURXZjM1ZpTFRnME9ETXdORE0yTlRjNE5GOVVNWGN1Ym1scExtZDZJaXdpZEhKbFpWOXZhV1FpT2lKa09EVmhaalU0WkRoaU1qZzJOamN3Wmpka01URmpPRGxqWkdFeU1UVXhZV0V5T1RrMk5XTm1JbjA9OjM4MDAw "HTTP/1.1 200 OK"
[2026-09-18 19:13:31,494][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjJZV3d2WkdWeWFYWmhkR2wyWlhNdmMzVmlMVEU0TmprMk5UZzVNVGcwTXk5elpYTXRNUzl6ZFdJdE1UZzJPVFkxT0RreE9EUXpYM0J5WlhCeWIyTXRjWFZoYzJseVlYZGZWREYzTG01d2VTSXNJblJ5WldWZmIybGtJam9pWkRnMVlXWTFPR1E0WWpJNE5qWTNNR1kzWkRFeFl6ZzVZMlJoTWpFMU1XRmhNams1TmpWalppSjk6MzkwMDA%3D "HTTP/1.1 200 OK"
[2026-09-18 19:13:31,707][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjJZV3d2WkdWeWFYWmhkR2wyWlhNdmMzVmlMVFF3TURBeU5UTXpNREl6Tnk5elpYTXRNUzl6ZFdJdE5EQXdNREkxTXpNd01qTTNYM0J5WlhCeWIyTXRabkpsWlhOMWNtWmxjbDlrWlhOakxXUmxjM1J5YVdWMWVGOVNUMGt1Ym5CNUlpd2lkSEpsWlY5dmFXUWlPaUprT0RWaFpqVTRaRGhpTWpnMk5qY3daamRrTVRGak9EbGpaR0V5TVRVeFlXRXlPVGsyTldObUluMD06NDAwMDA%3D "HTTP/1.1 200 OK"
[2026-09-18 19:13:31,919][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjJZV3d2WkdWeWFYWmhkR2wyWlhNdmMzVmlMVFU1TkRReE5qUXpNRGt5Tmk5elpYTXRNUzl6ZFdJdE5UazBOREUyTkRNd09USTJYM0J5WlhCeWIyTXRZMkYwTVRKMlltMWZaR1Z6WXkxbmJWOVVNWGN1Ym5CNUlpd2lkSEpsWlY5dmFXUWlPaUprT0RWaFpqVTRaRGhpTWpnMk5qY3daamRrTVRGak9EbGpaR0V5TVRVeFlXRXlPVGsyTldObUluMD06NDEwMDA%3D "HTTP/1.1 200 OK"
[2026-09-18 19:13:32,197][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjJZV3d2WkdWeWFYWmhkR2wyWlhNdmMzVmlMVGM1T1RZNU1USTBOVEkxTWk5elpYTXRNUzl6ZFdJdE56azVOamt4TWpRMU1qVXlYM0J5WlhCeWIyTXRjWFZoYzJseVlYZGZWREYzTG01d2VTSXNJblJ5WldWZmIybGtJam9pWkRnMVlXWTFPR1E0WWpJNE5qWTNNR1kzWkRFeFl6ZzVZMlJoTWpFMU1XRmhNams1TmpWalppSjk6NDIwMDA%3D "HTTP/1.1 200 OK"
[2026-09-18 19:13:32,375][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/tree/8508cda68fea74f217926acbf46ee5863f8879d1?expand=false&recursive=true&limit=1000&cursor=ZXlKbWFXeGxYMjVoYldVaU9pSjJZV3d2WkdWeWFYWmhkR2wyWlhNdmMzVmlMVGszTnpVek5UUTNNamt5TkM5elpYTXRNUzl6ZFdJdE9UYzNOVE0xTkRjeU9USTBYM0J5WlhCeWIyTXRabkpsWlhOMWNtWmxjbDlrWlhOakxXUmxjM1J5YVdWMWVGOVNUMGt1Ym5CNUlpd2lkSEpsWlY5dmFXUWlPaUprT0RWaFpqVTRaRGhpTWpnMk5qY3daamRrTVRGak9EbGpaR0V5TVRVeFlXRXlPVGsyTldObUluMD06NDMwMDA%3D "HTTP/1.1 200 OK"
Fetching 1 files: 0%| | 0/1 [00:00<?, ?it/s][2026-09-18 19:13:32,699][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/datasets/benoit-dufumier/openBHB/xet-read-token/8508cda68fea74f217926acbf46ee5863f8879d1 "HTTP/1.1 200 OK"
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Fetching 1 files: 0%| | 0/1 [00:00<?, ?it/s][2026-09-18 19:13:34,042][httpx][INFO] - HTTP Request: HEAD https://huggingface.co/datasets/benoit-dufumier/openBHB/resolve/8508cda68fea74f217926acbf46ee5863f8879d1/val/derivatives/cat12vbm_roi/cat12vbm_roi_features.csv "HTTP/1.1 307 Temporary Redirect"
[2026-09-18 19:13:34,049][httpx][INFO] - HTTP Request: HEAD https://huggingface.co/api/resolve-cache/datasets/benoit-dufumier/openBHB/8508cda68fea74f217926acbf46ee5863f8879d1/val%2Fderivatives%2Fcat12vbm_roi%2Fcat12vbm_roi_features.csv?%2Fdatasets%2Fbenoit-dufumier%2FopenBHB%2Fresolve%2F8508cda68fea74f217926acbf46ee5863f8879d1%2Fval%2Fderivatives%2Fcat12vbm_roi%2Fcat12vbm_roi_features.csv=&etag=%2291c129e597abe6e023aa5bfc90b6e28b11f80c3b%22 "HTTP/1.1 200 OK"
[2026-09-18 19:13:34,059][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/resolve-cache/datasets/benoit-dufumier/openBHB/8508cda68fea74f217926acbf46ee5863f8879d1/val%2Fderivatives%2Fcat12vbm_roi%2Fcat12vbm_roi_features.csv?%2Fdatasets%2Fbenoit-dufumier%2FopenBHB%2Fresolve%2F8508cda68fea74f217926acbf46ee5863f8879d1%2Fval%2Fderivatives%2Fcat12vbm_roi%2Fcat12vbm_roi_features.csv=&etag=%2291c129e597abe6e023aa5bfc90b6e28b11f80c3b%22 "HTTP/1.1 200 OK"
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Fetching 1 files: 0%| | 0/1 [00:00<?, ?it/s][2026-09-18 19:13:34,925][httpx][INFO] - HTTP Request: HEAD https://huggingface.co/datasets/benoit-dufumier/openBHB/resolve/8508cda68fea74f217926acbf46ee5863f8879d1/val/derivatives/freesurfer_roi/desikan_roi_features.csv "HTTP/1.1 307 Temporary Redirect"
[2026-09-18 19:13:34,933][httpx][INFO] - HTTP Request: HEAD https://huggingface.co/api/resolve-cache/datasets/benoit-dufumier/openBHB/8508cda68fea74f217926acbf46ee5863f8879d1/val%2Fderivatives%2Ffreesurfer_roi%2Fdesikan_roi_features.csv?%2Fdatasets%2Fbenoit-dufumier%2FopenBHB%2Fresolve%2F8508cda68fea74f217926acbf46ee5863f8879d1%2Fval%2Fderivatives%2Ffreesurfer_roi%2Fdesikan_roi_features.csv=&etag=%22b095eaa457e5650914251a5d326923da3af98894%22 "HTTP/1.1 200 OK"
[2026-09-18 19:13:34,941][httpx][INFO] - HTTP Request: GET https://huggingface.co/api/resolve-cache/datasets/benoit-dufumier/openBHB/8508cda68fea74f217926acbf46ee5863f8879d1/val%2Fderivatives%2Ffreesurfer_roi%2Fdesikan_roi_features.csv?%2Fdatasets%2Fbenoit-dufumier%2FopenBHB%2Fresolve%2F8508cda68fea74f217926acbf46ee5863f8879d1%2Fval%2Fderivatives%2Ffreesurfer_roi%2Fdesikan_roi_features.csv=&etag=%22b095eaa457e5650914251a5d326923da3af98894%22 "HTTP/1.1 200 OK"
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/opt/hostedtoolcache/Python/3.12.14/x64/lib/python3.12/site-packages/pytorch_lightning/utilities/_pytree.py:21: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
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Epoch 0: 88%|████████▊ | 89/101 [00:08<00:01, 11.11it/s, v_num=0, loss/train=9.120]
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Epoch 0: 95%|█████████▌| 96/101 [00:08<00:00, 11.25it/s, v_num=0, loss/train=8.890]
Epoch 0: 96%|█████████▌| 97/101 [00:08<00:00, 11.05it/s, v_num=0, loss/train=8.890]
Epoch 0: 96%|█████████▌| 97/101 [00:08<00:00, 11.05it/s, v_num=0, loss/train=8.800]
Epoch 0: 97%|█████████▋| 98/101 [00:08<00:00, 11.16it/s, v_num=0, loss/train=8.800]
Epoch 0: 97%|█████████▋| 98/101 [00:08<00:00, 11.16it/s, v_num=0, loss/train=8.990]
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Epoch 0: 99%|█████████▉| 100/101 [00:09<00:00, 11.08it/s, v_num=0, loss/train=8.900]
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Epoch 0: 100%|██████████| 101/101 [00:09<00:00, 11.19it/s, v_num=0, loss/train=8.630]
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Validation DataLoader 0: 88%|████████▊ | 21/24 [00:02<00:00, 9.64it/s]
Validation DataLoader 0: 92%|█████████▏| 22/24 [00:02<00:00, 10.08it/s]
Validation DataLoader 0: 96%|█████████▌| 23/24 [00:02<00:00, 10.52it/s]
Validation DataLoader 0: 100%|██████████| 24/24 [00:02<00:00, 10.96it/s]
Epoch 0: 100%|██████████| 101/101 [00:11<00:00, 8.62it/s, v_num=0, loss/train=8.630, loss/val=8.920]
Epoch 0: 100%|██████████| 101/101 [00:11<00:00, 8.62it/s, v_num=0, loss/train=8.630, loss/val=8.920]
Epoch 0: 0%| | 0/101 [00:00<?, ?it/s, v_num=0, loss/train=8.630, loss/val=8.920]
Epoch 1: 0%| | 0/101 [00:00<?, ?it/s, v_num=0, loss/train=8.630, loss/val=8.920]
Epoch 1: 1%| | 1/101 [00:00<01:36, 1.04it/s, v_num=0, loss/train=8.630, loss/val=8.920]
Epoch 1: 1%| | 1/101 [00:00<01:36, 1.04it/s, v_num=0, loss/train=8.770, loss/val=8.920]
Epoch 1: 2%|▏ | 2/101 [00:00<00:48, 2.05it/s, v_num=0, loss/train=8.770, loss/val=8.920]
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Epoch 1: 6%|▌ | 6/101 [00:01<00:29, 3.18it/s, v_num=0, loss/train=8.830, loss/val=8.920]
Epoch 1: 7%|▋ | 7/101 [00:01<00:25, 3.69it/s, v_num=0, loss/train=8.830, loss/val=8.920]
Epoch 1: 7%|▋ | 7/101 [00:01<00:25, 3.68it/s, v_num=0, loss/train=8.900, loss/val=8.920]
Epoch 1: 8%|▊ | 8/101 [00:01<00:22, 4.19it/s, v_num=0, loss/train=8.900, loss/val=8.920]
Epoch 1: 8%|▊ | 8/101 [00:01<00:22, 4.19it/s, v_num=0, loss/train=8.830, loss/val=8.920]
Epoch 1: 9%|▉ | 9/101 [00:01<00:20, 4.50it/s, v_num=0, loss/train=8.830, loss/val=8.920]
Epoch 1: 9%|▉ | 9/101 [00:02<00:20, 4.50it/s, v_num=0, loss/train=8.810, loss/val=8.920]
Epoch 1: 10%|▉ | 10/101 [00:02<00:18, 4.80it/s, v_num=0, loss/train=8.810, loss/val=8.920]
Epoch 1: 10%|▉ | 10/101 [00:02<00:18, 4.80it/s, v_num=0, loss/train=9.000, loss/val=8.920]
Epoch 1: 11%|█ | 11/101 [00:02<00:17, 5.20it/s, v_num=0, loss/train=9.000, loss/val=8.920]
Epoch 1: 11%|█ | 11/101 [00:02<00:17, 5.19it/s, v_num=0, loss/train=8.900, loss/val=8.920]
Epoch 1: 12%|█▏ | 12/101 [00:02<00:15, 5.61it/s, v_num=0, loss/train=8.900, loss/val=8.920]
Epoch 1: 12%|█▏ | 12/101 [00:02<00:15, 5.61it/s, v_num=0, loss/train=9.050, loss/val=8.920]
Epoch 1: 13%|█▎ | 13/101 [00:02<00:16, 5.43it/s, v_num=0, loss/train=9.050, loss/val=8.920]
Epoch 1: 13%|█▎ | 13/101 [00:02<00:16, 5.42it/s, v_num=0, loss/train=8.940, loss/val=8.920]
Epoch 1: 14%|█▍ | 14/101 [00:02<00:14, 5.81it/s, v_num=0, loss/train=8.940, loss/val=8.920]
Epoch 1: 14%|█▍ | 14/101 [00:02<00:14, 5.81it/s, v_num=0, loss/train=9.150, loss/val=8.920]
Epoch 1: 15%|█▍ | 15/101 [00:02<00:13, 6.17it/s, v_num=0, loss/train=9.150, loss/val=8.920]
Epoch 1: 15%|█▍ | 15/101 [00:02<00:13, 6.17it/s, v_num=0, loss/train=8.920, loss/val=8.920]
Epoch 1: 16%|█▌ | 16/101 [00:02<00:12, 6.55it/s, v_num=0, loss/train=8.920, loss/val=8.920]
Epoch 1: 16%|█▌ | 16/101 [00:02<00:12, 6.55it/s, v_num=0, loss/train=8.760, loss/val=8.920]
Epoch 1: 17%|█▋ | 17/101 [00:02<00:14, 6.00it/s, v_num=0, loss/train=8.760, loss/val=8.920]
Epoch 1: 17%|█▋ | 17/101 [00:02<00:14, 6.00it/s, v_num=0, loss/train=8.960, loss/val=8.920]
Epoch 1: 18%|█▊ | 18/101 [00:02<00:13, 6.31it/s, v_num=0, loss/train=8.960, loss/val=8.920]
Epoch 1: 18%|█▊ | 18/101 [00:02<00:13, 6.31it/s, v_num=0, loss/train=9.020, loss/val=8.920]
Epoch 1: 19%|█▉ | 19/101 [00:02<00:12, 6.63it/s, v_num=0, loss/train=9.020, loss/val=8.920]
Epoch 1: 19%|█▉ | 19/101 [00:02<00:12, 6.63it/s, v_num=0, loss/train=8.930, loss/val=8.920]
Epoch 1: 20%|█▉ | 20/101 [00:02<00:11, 6.95it/s, v_num=0, loss/train=8.930, loss/val=8.920]
Epoch 1: 20%|█▉ | 20/101 [00:02<00:11, 6.94it/s, v_num=0, loss/train=8.960, loss/val=8.920]
Epoch 1: 21%|██ | 21/101 [00:03<00:11, 6.70it/s, v_num=0, loss/train=8.960, loss/val=8.920]
Epoch 1: 21%|██ | 21/101 [00:03<00:11, 6.70it/s, v_num=0, loss/train=8.990, loss/val=8.920]
Epoch 1: 22%|██▏ | 22/101 [00:03<00:11, 6.96it/s, v_num=0, loss/train=8.990, loss/val=8.920]
Epoch 1: 22%|██▏ | 22/101 [00:03<00:11, 6.96it/s, v_num=0, loss/train=8.920, loss/val=8.920]
Epoch 1: 23%|██▎ | 23/101 [00:03<00:10, 7.25it/s, v_num=0, loss/train=8.920, loss/val=8.920]
Epoch 1: 23%|██▎ | 23/101 [00:03<00:10, 7.25it/s, v_num=0, loss/train=8.860, loss/val=8.920]
Epoch 1: 24%|██▍ | 24/101 [00:03<00:10, 7.53it/s, v_num=0, loss/train=8.860, loss/val=8.920]
Epoch 1: 24%|██▍ | 24/101 [00:03<00:10, 7.53it/s, v_num=0, loss/train=8.810, loss/val=8.920]
Epoch 1: 25%|██▍ | 25/101 [00:03<00:11, 6.62it/s, v_num=0, loss/train=8.810, loss/val=8.920]
Epoch 1: 25%|██▍ | 25/101 [00:03<00:11, 6.62it/s, v_num=0, loss/train=9.000, loss/val=8.920]
Epoch 1: 26%|██▌ | 26/101 [00:03<00:10, 6.86it/s, v_num=0, loss/train=9.000, loss/val=8.920]
Epoch 1: 26%|██▌ | 26/101 [00:03<00:10, 6.86it/s, v_num=0, loss/train=9.010, loss/val=8.920]
Epoch 1: 27%|██▋ | 27/101 [00:03<00:10, 7.10it/s, v_num=0, loss/train=9.010, loss/val=8.920]
Epoch 1: 27%|██▋ | 27/101 [00:03<00:10, 7.10it/s, v_num=0, loss/train=8.860, loss/val=8.920]
Epoch 1: 28%|██▊ | 28/101 [00:03<00:09, 7.33it/s, v_num=0, loss/train=8.860, loss/val=8.920]
Epoch 1: 28%|██▊ | 28/101 [00:03<00:09, 7.32it/s, v_num=0, loss/train=8.850, loss/val=8.920]
Epoch 1: 29%|██▊ | 29/101 [00:03<00:09, 7.25it/s, v_num=0, loss/train=8.850, loss/val=8.920]
Epoch 1: 29%|██▊ | 29/101 [00:03<00:09, 7.25it/s, v_num=0, loss/train=9.000, loss/val=8.920]
Epoch 1: 30%|██▉ | 30/101 [00:04<00:09, 7.47it/s, v_num=0, loss/train=9.000, loss/val=8.920]
Epoch 1: 30%|██▉ | 30/101 [00:04<00:09, 7.47it/s, v_num=0, loss/train=8.820, loss/val=8.920]
Epoch 1: 31%|███ | 31/101 [00:04<00:09, 7.65it/s, v_num=0, loss/train=8.820, loss/val=8.920]
Epoch 1: 31%|███ | 31/101 [00:04<00:09, 7.65it/s, v_num=0, loss/train=9.020, loss/val=8.920]
Epoch 1: 32%|███▏ | 32/101 [00:04<00:08, 7.87it/s, v_num=0, loss/train=9.020, loss/val=8.920]
Epoch 1: 32%|███▏ | 32/101 [00:04<00:08, 7.87it/s, v_num=0, loss/train=8.840, loss/val=8.920]
Epoch 1: 33%|███▎ | 33/101 [00:04<00:08, 7.77it/s, v_num=0, loss/train=8.840, loss/val=8.920]
Epoch 1: 33%|███▎ | 33/101 [00:04<00:08, 7.76it/s, v_num=0, loss/train=8.940, loss/val=8.920]
Epoch 1: 34%|███▎ | 34/101 [00:04<00:08, 7.96it/s, v_num=0, loss/train=8.940, loss/val=8.920]
Epoch 1: 34%|███▎ | 34/101 [00:04<00:08, 7.95it/s, v_num=0, loss/train=9.110, loss/val=8.920]
Epoch 1: 35%|███▍ | 35/101 [00:04<00:08, 8.16it/s, v_num=0, loss/train=9.110, loss/val=8.920]
Epoch 1: 35%|███▍ | 35/101 [00:04<00:08, 8.16it/s, v_num=0, loss/train=8.860, loss/val=8.920]
Epoch 1: 36%|███▌ | 36/101 [00:04<00:07, 8.22it/s, v_num=0, loss/train=8.860, loss/val=8.920]
Epoch 1: 36%|███▌ | 36/101 [00:04<00:07, 8.21it/s, v_num=0, loss/train=8.690, loss/val=8.920]
Epoch 1: 37%|███▋ | 37/101 [00:04<00:07, 8.12it/s, v_num=0, loss/train=8.690, loss/val=8.920]
Epoch 1: 37%|███▋ | 37/101 [00:04<00:07, 8.12it/s, v_num=0, loss/train=8.890, loss/val=8.920]
Epoch 1: 38%|███▊ | 38/101 [00:04<00:07, 8.31it/s, v_num=0, loss/train=8.890, loss/val=8.920]
Epoch 1: 38%|███▊ | 38/101 [00:04<00:07, 8.31it/s, v_num=0, loss/train=8.720, loss/val=8.920]
Epoch 1: 39%|███▊ | 39/101 [00:04<00:07, 8.41it/s, v_num=0, loss/train=8.720, loss/val=8.920]
Epoch 1: 39%|███▊ | 39/101 [00:04<00:07, 8.41it/s, v_num=0, loss/train=8.820, loss/val=8.920]
Epoch 1: 40%|███▉ | 40/101 [00:04<00:07, 8.60it/s, v_num=0, loss/train=8.820, loss/val=8.920]
Epoch 1: 40%|███▉ | 40/101 [00:04<00:07, 8.59it/s, v_num=0, loss/train=8.930, loss/val=8.920]
Epoch 1: 41%|████ | 41/101 [00:04<00:07, 8.52it/s, v_num=0, loss/train=8.930, loss/val=8.920]
Epoch 1: 41%|████ | 41/101 [00:04<00:07, 8.52it/s, v_num=0, loss/train=8.690, loss/val=8.920]
Epoch 1: 42%|████▏ | 42/101 [00:04<00:06, 8.69it/s, v_num=0, loss/train=8.690, loss/val=8.920]
Epoch 1: 42%|████▏ | 42/101 [00:04<00:06, 8.69it/s, v_num=0, loss/train=8.840, loss/val=8.920]
Epoch 1: 43%|████▎ | 43/101 [00:04<00:06, 8.79it/s, v_num=0, loss/train=8.840, loss/val=8.920]
Epoch 1: 43%|████▎ | 43/101 [00:04<00:06, 8.79it/s, v_num=0, loss/train=8.780, loss/val=8.920]
Epoch 1: 44%|████▎ | 44/101 [00:04<00:06, 8.97it/s, v_num=0, loss/train=8.780, loss/val=8.920]
Epoch 1: 44%|████▎ | 44/101 [00:04<00:06, 8.97it/s, v_num=0, loss/train=8.650, loss/val=8.920]
Epoch 1: 45%|████▍ | 45/101 [00:05<00:06, 8.77it/s, v_num=0, loss/train=8.650, loss/val=8.920]
Epoch 1: 45%|████▍ | 45/101 [00:05<00:06, 8.77it/s, v_num=0, loss/train=8.840, loss/val=8.920]
Epoch 1: 46%|████▌ | 46/101 [00:05<00:06, 8.92it/s, v_num=0, loss/train=8.840, loss/val=8.920]
Epoch 1: 46%|████▌ | 46/101 [00:05<00:06, 8.92it/s, v_num=0, loss/train=8.900, loss/val=8.920]
Epoch 1: 47%|████▋ | 47/101 [00:05<00:06, 8.94it/s, v_num=0, loss/train=8.900, loss/val=8.920]
Epoch 1: 47%|████▋ | 47/101 [00:05<00:06, 8.94it/s, v_num=0, loss/train=8.880, loss/val=8.920]
Epoch 1: 48%|████▊ | 48/101 [00:05<00:05, 9.10it/s, v_num=0, loss/train=8.880, loss/val=8.920]
Epoch 1: 48%|████▊ | 48/101 [00:05<00:05, 9.10it/s, v_num=0, loss/train=8.850, loss/val=8.920]
Epoch 1: 49%|████▊ | 49/101 [00:05<00:05, 9.05it/s, v_num=0, loss/train=8.850, loss/val=8.920]
Epoch 1: 49%|████▊ | 49/101 [00:05<00:05, 9.04it/s, v_num=0, loss/train=8.780, loss/val=8.920]
Epoch 1: 50%|████▉ | 50/101 [00:05<00:05, 9.20it/s, v_num=0, loss/train=8.780, loss/val=8.920]
Epoch 1: 50%|████▉ | 50/101 [00:05<00:05, 9.20it/s, v_num=0, loss/train=8.860, loss/val=8.920]
Epoch 1: 50%|█████ | 51/101 [00:05<00:05, 9.08it/s, v_num=0, loss/train=8.860, loss/val=8.920]
Epoch 1: 50%|█████ | 51/101 [00:05<00:05, 9.07it/s, v_num=0, loss/train=8.910, loss/val=8.920]
Epoch 1: 51%|█████▏ | 52/101 [00:05<00:05, 9.23it/s, v_num=0, loss/train=8.910, loss/val=8.920]
Epoch 1: 51%|█████▏ | 52/101 [00:05<00:05, 9.23it/s, v_num=0, loss/train=9.000, loss/val=8.920]
Epoch 1: 52%|█████▏ | 53/101 [00:05<00:05, 9.15it/s, v_num=0, loss/train=9.000, loss/val=8.920]
Epoch 1: 52%|█████▏ | 53/101 [00:05<00:05, 9.14it/s, v_num=0, loss/train=8.690, loss/val=8.920]
Epoch 1: 53%|█████▎ | 54/101 [00:05<00:05, 9.26it/s, v_num=0, loss/train=8.690, loss/val=8.920]
Epoch 1: 53%|█████▎ | 54/101 [00:05<00:05, 9.26it/s, v_num=0, loss/train=8.890, loss/val=8.920]
Epoch 1: 54%|█████▍ | 55/101 [00:05<00:04, 9.36it/s, v_num=0, loss/train=8.890, loss/val=8.920]
Epoch 1: 54%|█████▍ | 55/101 [00:05<00:04, 9.36it/s, v_num=0, loss/train=8.850, loss/val=8.920]
Epoch 1: 55%|█████▌ | 56/101 [00:05<00:04, 9.50it/s, v_num=0, loss/train=8.850, loss/val=8.920]
Epoch 1: 55%|█████▌ | 56/101 [00:05<00:04, 9.50it/s, v_num=0, loss/train=8.970, loss/val=8.920]
Epoch 1: 56%|█████▋ | 57/101 [00:05<00:04, 9.60it/s, v_num=0, loss/train=8.970, loss/val=8.920]
Epoch 1: 56%|█████▋ | 57/101 [00:05<00:04, 9.59it/s, v_num=0, loss/train=9.130, loss/val=8.920]
Epoch 1: 57%|█████▋ | 58/101 [00:06<00:04, 9.62it/s, v_num=0, loss/train=9.130, loss/val=8.920]
Epoch 1: 57%|█████▋ | 58/101 [00:06<00:04, 9.62it/s, v_num=0, loss/train=8.990, loss/val=8.920]
Epoch 1: 58%|█████▊ | 59/101 [00:06<00:04, 9.44it/s, v_num=0, loss/train=8.990, loss/val=8.920]
Epoch 1: 58%|█████▊ | 59/101 [00:06<00:04, 9.44it/s, v_num=0, loss/train=9.010, loss/val=8.920]
Epoch 1: 59%|█████▉ | 60/101 [00:06<00:04, 9.56it/s, v_num=0, loss/train=9.010, loss/val=8.920]
Epoch 1: 59%|█████▉ | 60/101 [00:06<00:04, 9.56it/s, v_num=0, loss/train=8.840, loss/val=8.920]
Epoch 1: 60%|██████ | 61/101 [00:06<00:04, 9.52it/s, v_num=0, loss/train=8.840, loss/val=8.920]
Epoch 1: 60%|██████ | 61/101 [00:06<00:04, 9.52it/s, v_num=0, loss/train=9.040, loss/val=8.920]
Epoch 1: 61%|██████▏ | 62/101 [00:06<00:04, 9.63it/s, v_num=0, loss/train=9.040, loss/val=8.920]
Epoch 1: 61%|██████▏ | 62/101 [00:06<00:04, 9.63it/s, v_num=0, loss/train=8.880, loss/val=8.920]
Epoch 1: 62%|██████▏ | 63/101 [00:06<00:03, 9.71it/s, v_num=0, loss/train=8.880, loss/val=8.920]
Epoch 1: 62%|██████▏ | 63/101 [00:06<00:03, 9.71it/s, v_num=0, loss/train=8.780, loss/val=8.920]
Epoch 1: 63%|██████▎ | 64/101 [00:06<00:03, 9.83it/s, v_num=0, loss/train=8.780, loss/val=8.920]
Epoch 1: 63%|██████▎ | 64/101 [00:06<00:03, 9.83it/s, v_num=0, loss/train=8.850, loss/val=8.920]
Epoch 1: 64%|██████▍ | 65/101 [00:06<00:03, 9.88it/s, v_num=0, loss/train=8.850, loss/val=8.920]
Epoch 1: 64%|██████▍ | 65/101 [00:06<00:03, 9.88it/s, v_num=0, loss/train=9.060, loss/val=8.920]
Epoch 1: 65%|██████▌ | 66/101 [00:06<00:03, 10.01it/s, v_num=0, loss/train=9.060, loss/val=8.920]
Epoch 1: 65%|██████▌ | 66/101 [00:06<00:03, 10.01it/s, v_num=0, loss/train=8.970, loss/val=8.920]
Epoch 1: 66%|██████▋ | 67/101 [00:06<00:03, 9.93it/s, v_num=0, loss/train=8.970, loss/val=8.920]
Epoch 1: 66%|██████▋ | 67/101 [00:06<00:03, 9.92it/s, v_num=0, loss/train=8.900, loss/val=8.920]
Epoch 1: 67%|██████▋ | 68/101 [00:06<00:03, 10.03it/s, v_num=0, loss/train=8.900, loss/val=8.920]
Epoch 1: 67%|██████▋ | 68/101 [00:06<00:03, 10.03it/s, v_num=0, loss/train=8.650, loss/val=8.920]
Epoch 1: 68%|██████▊ | 69/101 [00:06<00:03, 9.97it/s, v_num=0, loss/train=8.650, loss/val=8.920]
Epoch 1: 68%|██████▊ | 69/101 [00:06<00:03, 9.97it/s, v_num=0, loss/train=8.780, loss/val=8.920]
Epoch 1: 69%|██████▉ | 70/101 [00:06<00:03, 10.09it/s, v_num=0, loss/train=8.780, loss/val=8.920]
Epoch 1: 69%|██████▉ | 70/101 [00:06<00:03, 10.09it/s, v_num=0, loss/train=8.630, loss/val=8.920]
Epoch 1: 70%|███████ | 71/101 [00:07<00:02, 10.14it/s, v_num=0, loss/train=8.630, loss/val=8.920]
Epoch 1: 70%|███████ | 71/101 [00:07<00:02, 10.13it/s, v_num=0, loss/train=8.770, loss/val=8.920]
Epoch 1: 71%|███████▏ | 72/101 [00:07<00:02, 10.24it/s, v_num=0, loss/train=8.770, loss/val=8.920]
Epoch 1: 71%|███████▏ | 72/101 [00:07<00:02, 10.24it/s, v_num=0, loss/train=8.990, loss/val=8.920]
Epoch 1: 72%|███████▏ | 73/101 [00:07<00:02, 10.07it/s, v_num=0, loss/train=8.990, loss/val=8.920]
Epoch 1: 72%|███████▏ | 73/101 [00:07<00:02, 10.07it/s, v_num=0, loss/train=9.070, loss/val=8.920]
Epoch 1: 73%|███████▎ | 74/101 [00:07<00:02, 10.19it/s, v_num=0, loss/train=9.070, loss/val=8.920]
Epoch 1: 73%|███████▎ | 74/101 [00:07<00:02, 10.19it/s, v_num=0, loss/train=8.850, loss/val=8.920]
Epoch 1: 74%|███████▍ | 75/101 [00:07<00:02, 10.20it/s, v_num=0, loss/train=8.850, loss/val=8.920]
Epoch 1: 74%|███████▍ | 75/101 [00:07<00:02, 10.19it/s, v_num=0, loss/train=9.110, loss/val=8.920]
Epoch 1: 75%|███████▌ | 76/101 [00:07<00:02, 10.29it/s, v_num=0, loss/train=9.110, loss/val=8.920]
Epoch 1: 75%|███████▌ | 76/101 [00:07<00:02, 10.29it/s, v_num=0, loss/train=8.840, loss/val=8.920]
Epoch 1: 76%|███████▌ | 77/101 [00:07<00:02, 10.30it/s, v_num=0, loss/train=8.840, loss/val=8.920]
Epoch 1: 76%|███████▌ | 77/101 [00:07<00:02, 10.30it/s, v_num=0, loss/train=8.900, loss/val=8.920]
Epoch 1: 77%|███████▋ | 78/101 [00:07<00:02, 10.40it/s, v_num=0, loss/train=8.900, loss/val=8.920]
Epoch 1: 77%|███████▋ | 78/101 [00:07<00:02, 10.40it/s, v_num=0, loss/train=8.920, loss/val=8.920]
Epoch 1: 78%|███████▊ | 79/101 [00:07<00:02, 10.39it/s, v_num=0, loss/train=8.920, loss/val=8.920]
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/opt/hostedtoolcache/Python/3.12.14/x64/lib/python3.12/site-packages/pytorch_lightning/utilities/_pytree.py:21: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
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Epoch 0: 69%|██████▉ | 70/101 [00:11<00:05, 6.11it/s, v_num=1, loss/train=9.790]
Epoch 0: 70%|███████ | 71/101 [00:11<00:04, 6.18it/s, v_num=1, loss/train=9.790]
Epoch 0: 70%|███████ | 71/101 [00:11<00:04, 6.18it/s, v_num=1, loss/train=9.830]
Epoch 0: 71%|███████▏ | 72/101 [00:11<00:04, 6.27it/s, v_num=1, loss/train=9.830]
Epoch 0: 71%|███████▏ | 72/101 [00:11<00:04, 6.27it/s, v_num=1, loss/train=9.620]
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Epoch 0: 72%|███████▏ | 73/101 [00:11<00:04, 6.35it/s, v_num=1, loss/train=9.660]
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Epoch 0: 73%|███████▎ | 74/101 [00:12<00:04, 5.99it/s, v_num=1, loss/train=9.690]
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Epoch 0: 76%|███████▌ | 77/101 [00:12<00:03, 6.22it/s, v_num=1, loss/train=9.460]
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Epoch 0: 84%|████████▍ | 85/101 [00:13<00:02, 6.27it/s, v_num=1, loss/train=9.780]
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Epoch 0: 85%|████████▌ | 86/101 [00:14<00:02, 6.08it/s, v_num=1, loss/train=10.00]
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Epoch 0: 86%|████████▌ | 87/101 [00:14<00:02, 6.14it/s, v_num=1, loss/train=9.110]
Epoch 0: 87%|████████▋ | 88/101 [00:14<00:02, 6.21it/s, v_num=1, loss/train=9.110]
Epoch 0: 87%|████████▋ | 88/101 [00:14<00:02, 6.21it/s, v_num=1, loss/train=9.670]
Epoch 0: 88%|████████▊ | 89/101 [00:14<00:01, 6.27it/s, v_num=1, loss/train=9.670]
Epoch 0: 88%|████████▊ | 89/101 [00:14<00:01, 6.27it/s, v_num=1, loss/train=10.10]
Epoch 0: 89%|████████▉ | 90/101 [00:14<00:01, 6.16it/s, v_num=1, loss/train=10.10]
Epoch 0: 89%|████████▉ | 90/101 [00:14<00:01, 6.16it/s, v_num=1, loss/train=9.550]
Epoch 0: 90%|█████████ | 91/101 [00:14<00:01, 6.22it/s, v_num=1, loss/train=9.550]
Epoch 0: 90%|█████████ | 91/101 [00:14<00:01, 6.22it/s, v_num=1, loss/train=9.930]
Epoch 0: 91%|█████████ | 92/101 [00:14<00:01, 6.24it/s, v_num=1, loss/train=9.930]
Epoch 0: 91%|█████████ | 92/101 [00:14<00:01, 6.24it/s, v_num=1, loss/train=9.740]
Epoch 0: 92%|█████████▏| 93/101 [00:14<00:01, 6.29it/s, v_num=1, loss/train=9.740]
Epoch 0: 92%|█████████▏| 93/101 [00:14<00:01, 6.29it/s, v_num=1, loss/train=9.660]
Epoch 0: 93%|█████████▎| 94/101 [00:15<00:01, 6.20it/s, v_num=1, loss/train=9.660]
Epoch 0: 93%|█████████▎| 94/101 [00:15<00:01, 6.20it/s, v_num=1, loss/train=9.390]
Epoch 0: 94%|█████████▍| 95/101 [00:15<00:00, 6.25it/s, v_num=1, loss/train=9.390]
Epoch 0: 94%|█████████▍| 95/101 [00:15<00:00, 6.25it/s, v_num=1, loss/train=9.640]
Epoch 0: 95%|█████████▌| 96/101 [00:15<00:00, 6.31it/s, v_num=1, loss/train=9.640]
Epoch 0: 95%|█████████▌| 96/101 [00:15<00:00, 6.31it/s, v_num=1, loss/train=9.870]
Epoch 0: 96%|█████████▌| 97/101 [00:15<00:00, 6.38it/s, v_num=1, loss/train=9.870]
Epoch 0: 96%|█████████▌| 97/101 [00:15<00:00, 6.37it/s, v_num=1, loss/train=9.300]
Epoch 0: 97%|█████████▋| 98/101 [00:15<00:00, 6.18it/s, v_num=1, loss/train=9.300]
Epoch 0: 97%|█████████▋| 98/101 [00:15<00:00, 6.18it/s, v_num=1, loss/train=9.750]
Epoch 0: 98%|█████████▊| 99/101 [00:15<00:00, 6.24it/s, v_num=1, loss/train=9.750]
Epoch 0: 98%|█████████▊| 99/101 [00:15<00:00, 6.24it/s, v_num=1, loss/train=9.720]
Epoch 0: 99%|█████████▉| 100/101 [00:15<00:00, 6.30it/s, v_num=1, loss/train=9.720]
Epoch 0: 99%|█████████▉| 100/101 [00:15<00:00, 6.30it/s, v_num=1, loss/train=9.540]
Epoch 0: 100%|██████████| 101/101 [00:15<00:00, 6.36it/s, v_num=1, loss/train=9.540]
Epoch 0: 100%|██████████| 101/101 [00:15<00:00, 6.36it/s, v_num=1, loss/train=9.240]
Validation: | | 0/? [00:00<?, ?it/s]
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Validation DataLoader 0: 8%|▊ | 2/24 [00:00<00:04, 5.17it/s]
Validation DataLoader 0: 12%|█▎ | 3/24 [00:00<00:02, 7.58it/s]
Validation DataLoader 0: 17%|█▋ | 4/24 [00:00<00:02, 9.95it/s]
Validation DataLoader 0: 21%|██ | 5/24 [00:00<00:02, 9.35it/s]
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Validation DataLoader 0: 42%|████▏ | 10/24 [00:01<00:01, 7.43it/s]
Validation DataLoader 0: 46%|████▌ | 11/24 [00:01<00:01, 8.12it/s]
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Validation DataLoader 0: 75%|███████▌ | 18/24 [00:02<00:00, 7.35it/s]
Validation DataLoader 0: 79%|███████▉ | 19/24 [00:02<00:00, 7.73it/s]
Validation DataLoader 0: 83%|████████▎ | 20/24 [00:02<00:00, 8.09it/s]
Validation DataLoader 0: 88%|████████▊ | 21/24 [00:02<00:00, 7.85it/s]
Validation DataLoader 0: 92%|█████████▏| 22/24 [00:02<00:00, 7.88it/s]
Validation DataLoader 0: 96%|█████████▌| 23/24 [00:02<00:00, 8.23it/s]
Validation DataLoader 0: 100%|██████████| 24/24 [00:02<00:00, 8.58it/s]
Epoch 0: 100%|██████████| 101/101 [00:19<00:00, 5.06it/s, v_num=1, loss/train=9.240, loss/val=9.600]
Epoch 0: 100%|██████████| 101/101 [00:19<00:00, 5.06it/s, v_num=1, loss/train=9.240, loss/val=9.600]
Epoch 0: 0%| | 0/101 [00:00<?, ?it/s, v_num=1, loss/train=9.240, loss/val=9.600]
Epoch 1: 0%| | 0/101 [00:00<?, ?it/s, v_num=1, loss/train=9.240, loss/val=9.600]
Epoch 1: 1%| | 1/101 [00:01<02:57, 0.56it/s, v_num=1, loss/train=9.240, loss/val=9.600]
Epoch 1: 1%| | 1/101 [00:01<02:57, 0.56it/s, v_num=1, loss/train=9.540, loss/val=9.600]
Epoch 1: 2%|▏ | 2/101 [00:02<01:44, 0.95it/s, v_num=1, loss/train=9.540, loss/val=9.600]
Epoch 1: 2%|▏ | 2/101 [00:02<01:44, 0.95it/s, v_num=1, loss/train=9.430, loss/val=9.600]
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Epoch 1: 8%|▊ | 8/101 [00:02<00:31, 2.91it/s, v_num=1, loss/train=9.830, loss/val=9.600]
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Epoch 1: 10%|▉ | 10/101 [00:02<00:25, 3.57it/s, v_num=1, loss/train=9.260, loss/val=9.600]
Epoch 1: 11%|█ | 11/101 [00:02<00:24, 3.74it/s, v_num=1, loss/train=9.260, loss/val=9.600]
Epoch 1: 11%|█ | 11/101 [00:02<00:24, 3.74it/s, v_num=1, loss/train=9.720, loss/val=9.600]
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Epoch 1: 12%|█▏ | 12/101 [00:02<00:22, 4.04it/s, v_num=1, loss/train=9.620, loss/val=9.600]
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Epoch 1: 13%|█▎ | 13/101 [00:03<00:22, 3.93it/s, v_num=1, loss/train=9.470, loss/val=9.600]
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Epoch 1: 14%|█▍ | 14/101 [00:03<00:20, 4.17it/s, v_num=1, loss/train=9.430, loss/val=9.600]
Epoch 1: 15%|█▍ | 15/101 [00:03<00:21, 3.94it/s, v_num=1, loss/train=9.430, loss/val=9.600]
Epoch 1: 15%|█▍ | 15/101 [00:03<00:21, 3.94it/s, v_num=1, loss/train=9.790, loss/val=9.600]
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Epoch 1: 16%|█▌ | 16/101 [00:03<00:21, 4.01it/s, v_num=1, loss/train=9.800, loss/val=9.600]
Epoch 1: 17%|█▋ | 17/101 [00:04<00:19, 4.23it/s, v_num=1, loss/train=9.800, loss/val=9.600]
Epoch 1: 17%|█▋ | 17/101 [00:04<00:19, 4.22it/s, v_num=1, loss/train=9.830, loss/val=9.600]
Epoch 1: 18%|█▊ | 18/101 [00:04<00:18, 4.37it/s, v_num=1, loss/train=9.830, loss/val=9.600]
Epoch 1: 18%|█▊ | 18/101 [00:04<00:18, 4.37it/s, v_num=1, loss/train=9.550, loss/val=9.600]
Epoch 1: 19%|█▉ | 19/101 [00:04<00:18, 4.39it/s, v_num=1, loss/train=9.550, loss/val=9.600]
Epoch 1: 19%|█▉ | 19/101 [00:04<00:18, 4.39it/s, v_num=1, loss/train=9.460, loss/val=9.600]
Epoch 1: 20%|█▉ | 20/101 [00:04<00:17, 4.52it/s, v_num=1, loss/train=9.460, loss/val=9.600]
Epoch 1: 20%|█▉ | 20/101 [00:04<00:17, 4.51it/s, v_num=1, loss/train=9.560, loss/val=9.600]
Epoch 1: 21%|██ | 21/101 [00:04<00:16, 4.72it/s, v_num=1, loss/train=9.560, loss/val=9.600]
Epoch 1: 21%|██ | 21/101 [00:04<00:16, 4.72it/s, v_num=1, loss/train=10.00, loss/val=9.600]
Epoch 1: 22%|██▏ | 22/101 [00:04<00:16, 4.92it/s, v_num=1, loss/train=10.00, loss/val=9.600]
Epoch 1: 22%|██▏ | 22/101 [00:04<00:16, 4.92it/s, v_num=1, loss/train=9.530, loss/val=9.600]
Epoch 1: 23%|██▎ | 23/101 [00:04<00:16, 4.85it/s, v_num=1, loss/train=9.530, loss/val=9.600]
Epoch 1: 23%|██▎ | 23/101 [00:04<00:16, 4.85it/s, v_num=1, loss/train=9.660, loss/val=9.600]
Epoch 1: 24%|██▍ | 24/101 [00:04<00:15, 4.88it/s, v_num=1, loss/train=9.660, loss/val=9.600]
Epoch 1: 24%|██▍ | 24/101 [00:04<00:15, 4.88it/s, v_num=1, loss/train=9.830, loss/val=9.600]
Epoch 1: 25%|██▍ | 25/101 [00:05<00:15, 4.98it/s, v_num=1, loss/train=9.830, loss/val=9.600]
Epoch 1: 25%|██▍ | 25/101 [00:05<00:15, 4.98it/s, v_num=1, loss/train=9.500, loss/val=9.600]
Epoch 1: 26%|██▌ | 26/101 [00:05<00:14, 5.06it/s, v_num=1, loss/train=9.500, loss/val=9.600]
Epoch 1: 26%|██▌ | 26/101 [00:05<00:14, 5.06it/s, v_num=1, loss/train=9.490, loss/val=9.600]
Epoch 1: 27%|██▋ | 27/101 [00:05<00:15, 4.63it/s, v_num=1, loss/train=9.490, loss/val=9.600]
Epoch 1: 27%|██▋ | 27/101 [00:05<00:15, 4.63it/s, v_num=1, loss/train=9.800, loss/val=9.600]
Epoch 1: 28%|██▊ | 28/101 [00:06<00:15, 4.63it/s, v_num=1, loss/train=9.800, loss/val=9.600]
Epoch 1: 28%|██▊ | 28/101 [00:06<00:15, 4.63it/s, v_num=1, loss/train=9.590, loss/val=9.600]
Epoch 1: 29%|██▊ | 29/101 [00:06<00:15, 4.78it/s, v_num=1, loss/train=9.590, loss/val=9.600]
Epoch 1: 29%|██▊ | 29/101 [00:06<00:15, 4.78it/s, v_num=1, loss/train=9.590, loss/val=9.600]
Epoch 1: 30%|██▉ | 30/101 [00:06<00:14, 4.93it/s, v_num=1, loss/train=9.590, loss/val=9.600]
Epoch 1: 30%|██▉ | 30/101 [00:06<00:14, 4.92it/s, v_num=1, loss/train=9.490, loss/val=9.600]
Epoch 1: 31%|███ | 31/101 [00:06<00:13, 5.05it/s, v_num=1, loss/train=9.490, loss/val=9.600]
Epoch 1: 31%|███ | 31/101 [00:06<00:13, 5.05it/s, v_num=1, loss/train=9.570, loss/val=9.600]
Epoch 1: 32%|███▏ | 32/101 [00:06<00:13, 4.98it/s, v_num=1, loss/train=9.570, loss/val=9.600]
Epoch 1: 32%|███▏ | 32/101 [00:06<00:13, 4.98it/s, v_num=1, loss/train=9.320, loss/val=9.600]
Epoch 1: 33%|███▎ | 33/101 [00:06<00:13, 5.07it/s, v_num=1, loss/train=9.320, loss/val=9.600]
Epoch 1: 33%|███▎ | 33/101 [00:06<00:13, 5.07it/s, v_num=1, loss/train=9.730, loss/val=9.600]
Epoch 1: 34%|███▎ | 34/101 [00:06<00:12, 5.19it/s, v_num=1, loss/train=9.730, loss/val=9.600]
Epoch 1: 34%|███▎ | 34/101 [00:06<00:12, 5.19it/s, v_num=1, loss/train=9.820, loss/val=9.600]
Epoch 1: 35%|███▍ | 35/101 [00:06<00:12, 5.19it/s, v_num=1, loss/train=9.820, loss/val=9.600]
Epoch 1: 35%|███▍ | 35/101 [00:06<00:12, 5.19it/s, v_num=1, loss/train=9.390, loss/val=9.600]
Epoch 1: 36%|███▌ | 36/101 [00:06<00:12, 5.31it/s, v_num=1, loss/train=9.390, loss/val=9.600]
Epoch 1: 36%|███▌ | 36/101 [00:06<00:12, 5.31it/s, v_num=1, loss/train=9.650, loss/val=9.600]
Epoch 1: 37%|███▋ | 37/101 [00:07<00:12, 5.24it/s, v_num=1, loss/train=9.650, loss/val=9.600]
Epoch 1: 37%|███▋ | 37/101 [00:07<00:12, 5.24it/s, v_num=1, loss/train=9.640, loss/val=9.600]
Epoch 1: 38%|███▊ | 38/101 [00:07<00:11, 5.34it/s, v_num=1, loss/train=9.640, loss/val=9.600]
Epoch 1: 38%|███▊ | 38/101 [00:07<00:11, 5.34it/s, v_num=1, loss/train=9.350, loss/val=9.600]
Epoch 1: 39%|███▊ | 39/101 [00:07<00:11, 5.23it/s, v_num=1, loss/train=9.350, loss/val=9.600]
Epoch 1: 39%|███▊ | 39/101 [00:07<00:11, 5.23it/s, v_num=1, loss/train=9.720, loss/val=9.600]
Epoch 1: 40%|███▉ | 40/101 [00:07<00:11, 5.22it/s, v_num=1, loss/train=9.720, loss/val=9.600]
Epoch 1: 40%|███▉ | 40/101 [00:07<00:11, 5.22it/s, v_num=1, loss/train=9.450, loss/val=9.600]
Epoch 1: 41%|████ | 41/101 [00:07<00:11, 5.34it/s, v_num=1, loss/train=9.450, loss/val=9.600]
Epoch 1: 41%|████ | 41/101 [00:07<00:11, 5.34it/s, v_num=1, loss/train=9.450, loss/val=9.600]
Epoch 1: 42%|████▏ | 42/101 [00:07<00:10, 5.46it/s, v_num=1, loss/train=9.450, loss/val=9.600]
Epoch 1: 42%|████▏ | 42/101 [00:07<00:10, 5.46it/s, v_num=1, loss/train=9.800, loss/val=9.600]
Epoch 1: 43%|████▎ | 43/101 [00:08<00:10, 5.34it/s, v_num=1, loss/train=9.800, loss/val=9.600]
Epoch 1: 43%|████▎ | 43/101 [00:08<00:10, 5.34it/s, v_num=1, loss/train=9.560, loss/val=9.600]
Epoch 1: 44%|████▎ | 44/101 [00:08<00:10, 5.41it/s, v_num=1, loss/train=9.560, loss/val=9.600]
Epoch 1: 44%|████▎ | 44/101 [00:08<00:10, 5.41it/s, v_num=1, loss/train=9.520, loss/val=9.600]
Epoch 1: 45%|████▍ | 45/101 [00:08<00:10, 5.25it/s, v_num=1, loss/train=9.520, loss/val=9.600]
Epoch 1: 45%|████▍ | 45/101 [00:08<00:10, 5.25it/s, v_num=1, loss/train=9.560, loss/val=9.600]
Epoch 1: 46%|████▌ | 46/101 [00:08<00:10, 5.16it/s, v_num=1, loss/train=9.560, loss/val=9.600]
Epoch 1: 46%|████▌ | 46/101 [00:08<00:10, 5.16it/s, v_num=1, loss/train=9.510, loss/val=9.600]
Epoch 1: 47%|████▋ | 47/101 [00:09<00:10, 5.07it/s, v_num=1, loss/train=9.510, loss/val=9.600]
Epoch 1: 47%|████▋ | 47/101 [00:09<00:10, 5.07it/s, v_num=1, loss/train=9.210, loss/val=9.600]
Epoch 1: 48%|████▊ | 48/101 [00:09<00:10, 5.17it/s, v_num=1, loss/train=9.210, loss/val=9.600]
Epoch 1: 48%|████▊ | 48/101 [00:09<00:10, 5.17it/s, v_num=1, loss/train=9.480, loss/val=9.600]
Epoch 1: 49%|████▊ | 49/101 [00:09<00:09, 5.26it/s, v_num=1, loss/train=9.480, loss/val=9.600]
Epoch 1: 49%|████▊ | 49/101 [00:09<00:09, 5.26it/s, v_num=1, loss/train=9.610, loss/val=9.600]
Epoch 1: 50%|████▉ | 50/101 [00:09<00:09, 5.35it/s, v_num=1, loss/train=9.610, loss/val=9.600]
Epoch 1: 50%|████▉ | 50/101 [00:09<00:09, 5.35it/s, v_num=1, loss/train=9.650, loss/val=9.600]
Epoch 1: 50%|█████ | 51/101 [00:09<00:09, 5.39it/s, v_num=1, loss/train=9.650, loss/val=9.600]
Epoch 1: 50%|█████ | 51/101 [00:09<00:09, 5.39it/s, v_num=1, loss/train=9.840, loss/val=9.600]
Epoch 1: 51%|█████▏ | 52/101 [00:09<00:09, 5.41it/s, v_num=1, loss/train=9.840, loss/val=9.600]
Epoch 1: 51%|█████▏ | 52/101 [00:09<00:09, 5.41it/s, v_num=1, loss/train=9.470, loss/val=9.600]
Epoch 1: 52%|█████▏ | 53/101 [00:09<00:08, 5.51it/s, v_num=1, loss/train=9.470, loss/val=9.600]
Epoch 1: 52%|█████▏ | 53/101 [00:09<00:08, 5.50it/s, v_num=1, loss/train=9.730, loss/val=9.600]
Epoch 1: 53%|█████▎ | 54/101 [00:09<00:08, 5.60it/s, v_num=1, loss/train=9.730, loss/val=9.600]
Epoch 1: 53%|█████▎ | 54/101 [00:09<00:08, 5.59it/s, v_num=1, loss/train=9.700, loss/val=9.600]
Epoch 1: 54%|█████▍ | 55/101 [00:10<00:08, 5.41it/s, v_num=1, loss/train=9.700, loss/val=9.600]
Epoch 1: 54%|█████▍ | 55/101 [00:10<00:08, 5.41it/s, v_num=1, loss/train=9.330, loss/val=9.600]
Epoch 1: 55%|█████▌ | 56/101 [00:10<00:08, 5.40it/s, v_num=1, loss/train=9.330, loss/val=9.600]
Epoch 1: 55%|█████▌ | 56/101 [00:10<00:08, 5.40it/s, v_num=1, loss/train=9.370, loss/val=9.600]
Epoch 1: 56%|█████▋ | 57/101 [00:10<00:08, 5.49it/s, v_num=1, loss/train=9.370, loss/val=9.600]
Epoch 1: 56%|█████▋ | 57/101 [00:10<00:08, 5.49it/s, v_num=1, loss/train=9.630, loss/val=9.600]
Epoch 1: 57%|█████▋ | 58/101 [00:10<00:07, 5.58it/s, v_num=1, loss/train=9.630, loss/val=9.600]
Epoch 1: 57%|█████▋ | 58/101 [00:10<00:07, 5.58it/s, v_num=1, loss/train=9.410, loss/val=9.600]
Epoch 1: 58%|█████▊ | 59/101 [00:10<00:07, 5.53it/s, v_num=1, loss/train=9.410, loss/val=9.600]
Epoch 1: 58%|█████▊ | 59/101 [00:10<00:07, 5.53it/s, v_num=1, loss/train=9.590, loss/val=9.600]
Epoch 1: 59%|█████▉ | 60/101 [00:10<00:07, 5.61it/s, v_num=1, loss/train=9.590, loss/val=9.600]
Epoch 1: 59%|█████▉ | 60/101 [00:10<00:07, 5.61it/s, v_num=1, loss/train=9.650, loss/val=9.600]
Epoch 1: 60%|██████ | 61/101 [00:11<00:07, 5.49it/s, v_num=1, loss/train=9.650, loss/val=9.600]
Epoch 1: 60%|██████ | 61/101 [00:11<00:07, 5.49it/s, v_num=1, loss/train=9.220, loss/val=9.600]
Epoch 1: 61%|██████▏ | 62/101 [00:11<00:07, 5.57it/s, v_num=1, loss/train=9.220, loss/val=9.600]
Epoch 1: 61%|██████▏ | 62/101 [00:11<00:07, 5.57it/s, v_num=1, loss/train=9.280, loss/val=9.600]
Epoch 1: 62%|██████▏ | 63/101 [00:11<00:06, 5.50it/s, v_num=1, loss/train=9.280, loss/val=9.600]
Epoch 1: 62%|██████▏ | 63/101 [00:11<00:06, 5.50it/s, v_num=1, loss/train=9.450, loss/val=9.600]
Epoch 1: 63%|██████▎ | 64/101 [00:11<00:06, 5.55it/s, v_num=1, loss/train=9.450, loss/val=9.600]
Epoch 1: 63%|██████▎ | 64/101 [00:11<00:06, 5.55it/s, v_num=1, loss/train=9.420, loss/val=9.600]
Epoch 1: 64%|██████▍ | 65/101 [00:11<00:06, 5.63it/s, v_num=1, loss/train=9.420, loss/val=9.600]
Epoch 1: 64%|██████▍ | 65/101 [00:11<00:06, 5.63it/s, v_num=1, loss/train=9.220, loss/val=9.600]
Epoch 1: 65%|██████▌ | 66/101 [00:11<00:06, 5.71it/s, v_num=1, loss/train=9.220, loss/val=9.600]
Epoch 1: 65%|██████▌ | 66/101 [00:11<00:06, 5.71it/s, v_num=1, loss/train=9.250, loss/val=9.600]
Epoch 1: 66%|██████▋ | 67/101 [00:12<00:06, 5.57it/s, v_num=1, loss/train=9.250, loss/val=9.600]
Epoch 1: 66%|██████▋ | 67/101 [00:12<00:06, 5.57it/s, v_num=1, loss/train=9.340, loss/val=9.600]
Epoch 1: 67%|██████▋ | 68/101 [00:12<00:05, 5.63it/s, v_num=1, loss/train=9.340, loss/val=9.600]
Epoch 1: 67%|██████▋ | 68/101 [00:12<00:05, 5.63it/s, v_num=1, loss/train=9.240, loss/val=9.600]
Epoch 1: 68%|██████▊ | 69/101 [00:12<00:05, 5.70it/s, v_num=1, loss/train=9.240, loss/val=9.600]
Epoch 1: 68%|██████▊ | 69/101 [00:12<00:05, 5.70it/s, v_num=1, loss/train=9.690, loss/val=9.600]
Epoch 1: 69%|██████▉ | 70/101 [00:12<00:05, 5.78it/s, v_num=1, loss/train=9.690, loss/val=9.600]
Epoch 1: 69%|██████▉ | 70/101 [00:12<00:05, 5.78it/s, v_num=1, loss/train=9.630, loss/val=9.600]
Epoch 1: 70%|███████ | 71/101 [00:12<00:05, 5.74it/s, v_num=1, loss/train=9.630, loss/val=9.600]
Epoch 1: 70%|███████ | 71/101 [00:12<00:05, 5.74it/s, v_num=1, loss/train=9.450, loss/val=9.600]
Epoch 1: 71%|███████▏ | 72/101 [00:12<00:05, 5.77it/s, v_num=1, loss/train=9.450, loss/val=9.600]
Epoch 1: 71%|███████▏ | 72/101 [00:12<00:05, 5.77it/s, v_num=1, loss/train=9.450, loss/val=9.600]
Epoch 1: 72%|███████▏ | 73/101 [00:12<00:04, 5.73it/s, v_num=1, loss/train=9.450, loss/val=9.600]
Epoch 1: 72%|███████▏ | 73/101 [00:12<00:04, 5.73it/s, v_num=1, loss/train=9.450, loss/val=9.600]
Epoch 1: 73%|███████▎ | 74/101 [00:12<00:04, 5.76it/s, v_num=1, loss/train=9.450, loss/val=9.600]
Epoch 1: 73%|███████▎ | 74/101 [00:12<00:04, 5.76it/s, v_num=1, loss/train=9.830, loss/val=9.600]
Epoch 1: 74%|███████▍ | 75/101 [00:13<00:04, 5.67it/s, v_num=1, loss/train=9.830, loss/val=9.600]
Epoch 1: 74%|███████▍ | 75/101 [00:13<00:04, 5.67it/s, v_num=1, loss/train=9.530, loss/val=9.600]
Epoch 1: 75%|███████▌ | 76/101 [00:13<00:04, 5.66it/s, v_num=1, loss/train=9.530, loss/val=9.600]
Epoch 1: 75%|███████▌ | 76/101 [00:13<00:04, 5.66it/s, v_num=1, loss/train=9.680, loss/val=9.600]
Epoch 1: 76%|███████▌ | 77/101 [00:13<00:04, 5.72it/s, v_num=1, loss/train=9.680, loss/val=9.600]
Epoch 1: 76%|███████▌ | 77/101 [00:13<00:04, 5.72it/s, v_num=1, loss/train=9.710, loss/val=9.600]
Epoch 1: 77%|███████▋ | 78/101 [00:13<00:03, 5.78it/s, v_num=1, loss/train=9.710, loss/val=9.600]
Epoch 1: 77%|███████▋ | 78/101 [00:13<00:03, 5.78it/s, v_num=1, loss/train=9.530, loss/val=9.600]
Epoch 1: 78%|███████▊ | 79/101 [00:13<00:03, 5.74it/s, v_num=1, loss/train=9.530, loss/val=9.600]
Epoch 1: 78%|███████▊ | 79/101 [00:13<00:03, 5.74it/s, v_num=1, loss/train=9.350, loss/val=9.600]
Epoch 1: 79%|███████▉ | 80/101 [00:13<00:03, 5.75it/s, v_num=1, loss/train=9.350, loss/val=9.600]
Epoch 1: 79%|███████▉ | 80/101 [00:13<00:03, 5.74it/s, v_num=1, loss/train=9.710, loss/val=9.600]
Epoch 1: 80%|████████ | 81/101 [00:13<00:03, 5.81it/s, v_num=1, loss/train=9.710, loss/val=9.600]
Epoch 1: 80%|████████ | 81/101 [00:13<00:03, 5.81it/s, v_num=1, loss/train=9.300, loss/val=9.600]
Epoch 1: 81%|████████ | 82/101 [00:13<00:03, 5.88it/s, v_num=1, loss/train=9.300, loss/val=9.600]
Epoch 1: 81%|████████ | 82/101 [00:13<00:03, 5.88it/s, v_num=1, loss/train=9.560, loss/val=9.600]
Epoch 1: 82%|████████▏ | 83/101 [00:14<00:03, 5.83it/s, v_num=1, loss/train=9.560, loss/val=9.600]
Epoch 1: 82%|████████▏ | 83/101 [00:14<00:03, 5.83it/s, v_num=1, loss/train=9.500, loss/val=9.600]
Epoch 1: 83%|████████▎ | 84/101 [00:14<00:02, 5.88it/s, v_num=1, loss/train=9.500, loss/val=9.600]
Epoch 1: 83%|████████▎ | 84/101 [00:14<00:02, 5.88it/s, v_num=1, loss/train=9.510, loss/val=9.600]
Epoch 1: 84%|████████▍ | 85/101 [00:14<00:02, 5.85it/s, v_num=1, loss/train=9.510, loss/val=9.600]
Epoch 1: 84%|████████▍ | 85/101 [00:14<00:02, 5.85it/s, v_num=1, loss/train=9.630, loss/val=9.600]
Epoch 1: 85%|████████▌ | 86/101 [00:14<00:02, 5.90it/s, v_num=1, loss/train=9.630, loss/val=9.600]
Epoch 1: 85%|████████▌ | 86/101 [00:14<00:02, 5.90it/s, v_num=1, loss/train=9.310, loss/val=9.600]
Epoch 1: 86%|████████▌ | 87/101 [00:15<00:02, 5.77it/s, v_num=1, loss/train=9.310, loss/val=9.600]
Epoch 1: 86%|████████▌ | 87/101 [00:15<00:02, 5.77it/s, v_num=1, loss/train=9.820, loss/val=9.600]
Epoch 1: 87%|████████▋ | 88/101 [00:15<00:02, 5.77it/s, v_num=1, loss/train=9.820, loss/val=9.600]
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/opt/hostedtoolcache/Python/3.12.14/x64/lib/python3.12/site-packages/pytorch_lightning/utilities/_pytree.py:21: `isinstance(treespec, LeafSpec)` is deprecated, use `isinstance(treespec, TreeSpec) and treespec.is_leaf()` instead.
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Z shapes - VBM: torch.Size([757, 32]), SBM: torch.Size([757, 32])
Total running time of the script: (1 minutes 28.388 seconds)
Estimated memory usage: 166 MB